We need to drop the robots-are-taking our jobs mindset

Data61's Adrian Turner: The key to achieving long-term structural change from AI lies in the power of emerging systems to learn and modify themselves without human intervention.
Christopher Pearce

by
Adrian Turner

Wild animals aren't usually paid to appear in ads but if The Lion's Share initiative succeeds, that will change.

Elephants, tigers and other animals could receive more than $100 million in donations from advertisers over the next three years

The Lion's Share Fund was just announced at the Cannes International Advertising Festival with Special Ambassador David Attenborough, and will be launched at the UN General Assembly in September.

Conceived by Australian production company FINCH, it will use a subset of artificial intelligence (AI) to identify animals in ads.

“Animals are in 20 percent of all advertisements we see,” says Lion’s Share Special Ambassador Sir David Attenborough.
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Advertisers will be asked to put 0.5 per cent of their media spend from ads involving animals towards the fund, which is then spent on wildlife conservation and animal welfare programs around the world.

It builds on Data61 work already under way to track fish in the Great Barrier Reef, monitor biodiversity in the Amazon and deter elephants from entering African villages using sensors.

These are examples of AI being used for good and to create new value that didn't previously exist, with profound benefits to the economy, society and the environment. They're also perfect examples of why our current AI conversation is all wrong.

It's long been touted, in numerous reports from countless consulting firms, that AI will increase efficiency and replace mundane tasks with a computer program that is less taxing on both their bottom lines and human resource departments.

Incrementalism and the pursuit of productivity gains is the reason many companies decide to make their data processing staff redundant, hire data scientists and shrink their overall workforce in an effort to please shareholders.

Kangaroo spotting

At some level these consultants and companies are right, but the emphasis on incremental efficiency gains is short-sighted.

They don't embrace the true promise of AI to create new-to-the-world value; whether it's from identifying patterns in data and making new connections, scenario-modelling to make predictions that inform better decision-making, rewiring entire value chains, or even redrawing industry boundaries.

The key to achieving long-term structural change from AI lies in the power of these emerging systems to learn and modify themselves without human intervention.

They are closed loop: that is, they make predictions about the future and monitor to learn if these are accurate before modifying themselves accordingly without human intervention. Always on, always learning at unprecedented scale. All fuelled by data.

Case in point, we were taught to drive one-on-one before computer vision technology was introduced. Now, computer vision systems in cars learn from the experiences of the manufacturer's entire fleet.

The first encounter with a kangaroo mislabels it as a dog, the car uses machine learning – a subset of AI – to match images on the internet, and within hours every car in the fleet around the world can recognise a kangaroo.

These systems will ultimately make self-driving cars safer than the way we drive as individuals without this technology, and force a restructure of the car insurance industry.

Capital for good ideas

In banking, learning systems can more accurately quantify credit worthiness and open up lending to the 1.4 billion people in emerging economies who are currently unbanked, boosting GDP in these economies by $4.8 trillion by 2025.

In healthcare, companies like Virtus Health and Harrison-AI are already developing AI that scans time-lapse videos of embryos during development to identify those with the greatest chance of survival.

Sydney University-based Q-CTRL is using machine learning for Quantum computing software to keep the atom scale bits – Qubits – stable long enough for them to be used for computational tasks.

At Data61 we've used machine learning to help produce a new national-scale, high-fidelity soil map for farmers and their underwriters to better understand the potential profitability of farms.

We've also used machine learning for bushfire evacuation modelling and in cybersecurity systems. These aren't incremental productivity gains but industry and society-changing value creation.

But you wouldn't get any of this from the current national AI conversation. The conversation has been blunt and simplistic with an overarching meme that robots are taking our jobs, or we don't have the capability to control our destiny as a country in the face of this disruption.

While Australia is at risk of falling behind the pack when it comes to AI and technology innovation, and while it's true we can't outspend most other countries in AI R&D, our dilemma is more to do with our emphasis on the incremental than anything else. Perhaps even a lack of imagination.

There is an abundance of large unsolved data and AI-related problems as well as an abundance of global capital for good ideas, and the companies and industries that will grow from them. So what's an Australian leader to do?

Kodak moment

Industry and business leaders must forgo the short-sighted overemphasis on compliance and risk management, focus on creating value with a global growth mindset, lift the digital literacy of teams at all levels, and disrupt from within before others do.

Our leaders need to understand the second- and third-order consequences of AI and deeply understand new ways to solve existing and adjacent customer needs. Kodak misjudged and thought they were a photography company.

They weren't, they were a company that enabled sharing of memories. Through this filter it's conceivable Kodak had the brand equity and social licence to build the world's social network.

These sorts of differences are subtle but profound, and are often the difference between life and death as a company.

It's not just good strategic and economic sense to understand AI and its implications, it's arguably the fiduciary responsibility of our leaders to lead the change and new value creation it enables.

Data61 has been appointed by the government to lead the development of a national AI road map that will guide future national investments, AI and machine learning PhD scholarships and a national AI Ethics Framework.

We take that responsibility seriously and with humility. We recognise getting this right collectively as a country will ensure Australia's prosperity for generations and ultimately, improve our quality of life.

Our priority is to help Australia capture its fair share of the global opportunities of AI and by focusing on ethical new value creation, perhaps even a lion's share.